Multichannel SAR-GMTI Algorithm Based on Adaptive Data Reconstruction and Improved RPCA

doi:10.1109/TGRS.2025.3540100

	Multichannel SAR-GMTI Algorithm Based on Adaptive Data Reconstruction and Improved RPCA
	Liu, Kun 1; He, Xiongpeng 1; Liao, Guisheng 1; Zhu, Shengqi 1; Tan, Haining 2; Qiu, Jibing 2
	2025
发表期刊	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN	0196-2892
卷号	63 页码:16
摘要	In recent years, the low-rank matrix recovery theory has acquired widespread application in the radar system. For multichannel synthetic aperture radar systems, the robust principal component analysis (RPCA) has proven to be a valuable technique for effectively distinguishing moving targets from static background clutter within the image domain. However, in nonideal environments, the RPCA is susceptible to channel errors and strong clutter, resulting in degraded target detection performance. To resolve this issue, a slow ground-moving target indication (GMTI) processing algorithm is proposed in this article. First, the sample selection and data reconstruction (DR) are used to further compensate for channel imbalance error and registration error. Next, an RPCA optimization framework is proposed to mitigate the issue of elevated false alarm rates caused by heterogeneous environments, and the sparse matrix is obtained through the application of the alternating direction method of multipliers (ADMM). The proposed optimization model not only avoids excessive punishment of large singular values by kernel norm weighting but also further improves the performance of target detection by introducing a difference matrix and a Fourier matrix. Finally, the estimation of the target's radial velocity is accomplished through the utilization of the adaptive match filtering (AMF) algorithm. Compared with the traditional RPCA algorithm, the proposed algorithm significantly reduces the false alarm rate under the background of strong clutter. Theoretical analyses and measured data results verify the effectiveness of the proposed algorithm.
关键词	Adaptive match filtering (AMF) data recon- struction (DR) ground-moving target indication (GMTI) robust principal component analysis (RPCA) robust principal component analysis (RPCA) robust principal component analysis (RPCA)
DOI	10.1109/TGRS.2025.3540100
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China (NSFC)[62201408] ; National Natural Science Foundation of China (NSFC)[61931016] ; National Natural Science Foundation of China (NSFC)[61621005] ; National Natural Science Foundation of China (NSFC)[62431021]
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001434801400025
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/40697
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	He, Xiongpeng; Liao, Guisheng
作者单位	1.Xidian Univ, Natl Key Lab Radar Signal Proc, Xian 710071, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Res Ctr Intelligent Comp Syst, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Liu, Kun,He, Xiongpeng,Liao, Guisheng,et al. Multichannel SAR-GMTI Algorithm Based on Adaptive Data Reconstruction and Improved RPCA[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2025,63:16.
APA	Liu, Kun,He, Xiongpeng,Liao, Guisheng,Zhu, Shengqi,Tan, Haining,&Qiu, Jibing.(2025).Multichannel SAR-GMTI Algorithm Based on Adaptive Data Reconstruction and Improved RPCA.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,63,16.
MLA	Liu, Kun,et al."Multichannel SAR-GMTI Algorithm Based on Adaptive Data Reconstruction and Improved RPCA".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 63(2025):16.